code: def feedforward(inputs, num_units, scope="multihead_attention"):
      """Point-wise feed forward net.
  
      Args:
        inputs: A 3d tensor with shape of [N, T, C].
        num_units: A list of two integers.
        scope: Optional scope for `variable_scope`.
        reuse: Boolean, whether to reuse the weights of a previous layer
          by the same name.
  
      Returns:
        A 3d tensor with the same shape and dtype as inputs
      """
      with tf.variable_scope(scope):
          # Inner layer
          params = {
              "inputs": inputs,
              "filters": num_units[0],
              "kernel_size": 1,
              "activation": tf.nn.relu,
              "use_bias": True,
          }
          outputs = tf.layers.conv1d(**params)
  
          # Readout layer
          params = {
              "inputs": outputs,
              "filters": num_units[1],
              "kernel_size": 1,
              "activation": None,
              "use_bias": True,
          }
          outputs = tf.layers.conv1d(**params)
  
          # Residual connection
          outputs += inputs
  
          # Normalize
          outputs = normalize(outputs)
  
      return outputs
  
file_path: nni/examples/trials/weight_sharing/ga_squad/graph_to_tf.py
imports: import tensorflow
  from rnn import XGRUCell
  from util import dropout
  from graph import LayerType
label_desc: Use image node_id 4 for calling a global function with example usage: feedforward(inputs, num_units, scope) and returns: outputs